Fine-tuning vs RAG: the criterion
By default, RAG: when knowledge must stay current and citations matter. Fine-tuning when style, language or a narrow skill doesn't fit in context. Hybrid when both are true.
Fine-tuning and RAG don't compete: they solve different problems. This is the decision order we follow before spending a single peso on training.
RAG first, almost always
Changing knowledge and required citations: RAG.
If your knowledge changes (catalogues, policies, documentation) or your answers must carry citations, RAG is the default: index, re-rank and cite, without touching the model.
RAG fails when the problem isn't knowledge but form: tone, output format, or a narrow task where the generic model falls short of your quality bar. That's where fine-tuning comes in. If you want to decide which of the two fits your case, Slash supports companies with LLM fine-tuning and RAG.
Fine-tune for form, not facts
LoRA adapters for tone and format; facts stay in RAG.
Fine-tuning wins when you need the model to sound, format or execute like your organization: editorial style, fixed output structures, a narrow domain with stable rules.
At Slash we use LoRA adapters for that layer: we don't retrain the world. Cost stays bounded, rollback is possible, and knowledge keeps living in RAG, where it can be updated.
Hybrid, when both are true
Adapters for form, RAG for data.
When the style must be yours and the knowledge must stay current, hybrid wins: adapters for form, RAG for data. It's the most common configuration in Slash deployments and the cheapest to maintain.
Four questions before paying for training
Answer them in writing; the route follows.
Before paying for training, answer in writing: is the problem knowledge or form? Does your data change daily or stay stable? Must citations be verifiable? Must the knowledge stay in-house? The answers define the route; everything else is consultant margin.
Key takeaways
- RAG by default: changing knowledge and required citations.
- Fine-tune only for form: style, format and narrow tasks.
- Hybrid when both are true.
- Nothing is decided without a measured baseline.
- Local inference when compliance demands it.
Sources
Editorial note: this analysis reflects the public information available on the review date. Models, prices and rules change fast; every third-party figure links to its source, and our opinions are labeled as such. Spotted an error? Write to contact@slash-digital.io.
The questions we hear often
When is fine-tuning NOT worth it?
When the problem is missing or changing knowledge: RAG handles that with less cost and less risk.
What's a LoRA adapter?
A small layer trained on top of the base model for form and narrow tasks: cheap, reversible and compatible with local inference.
Can RAG and fine-tuning be combined?
Yes, that's the usual production pattern: adapters for form, RAG for cited knowledge.
How much does an experiment cost?
It's scoped in sprints and the diagnosis sets the size. Write to us with your case.
How is the outcome measured?
Against a baseline of the best generic model, with comparable quality, cost and latency metrics.
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